Knowledge Modeling for Treatment Planning and Its Clinical Implementation
Experience, knowledge, and guidelines about intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) have been accumulated over the past decade, but they have largely not been formally modeled to support more efficient and optimal treatment planning and even automate treatment planning in routine cases. In routine clinic practice, IMRT/VMAT treatment planning continues to be a time-consuming iterative process. Each patient presents a unique set of anatomic constraints on how much the dose can be “sculpted” to spare normal tissue, which is currently unknown to the planner. Balancing the competing goalsof target coverage and organs at risk (OARs) sparing is a trial-and-error process guided largely by the planner’s and physician’s personal experience, skill, and knowledge. Further, for any given dose distribution, likely clinical outcomes (e.g. tumor control rate and normal tissue toxicity) are not readily apparent to physicians. Thus, there is a strong need to explicitly organize, model and integrate the available knowledge from various sources into the planning process. We will demonstrate how integration of readily-accessible knowledge into the planning process will improve the efficiency of IMRT/VMAT planning and even automate the planning process completely for certain routine cases. This presents an exciting opportunity to reduce the planning cost while improving the overall quality of resulting plans. We will summarize the state-of-the-art in current approaches for planning knowledge modeling and existing algorithms for automatic generation of best achievable plans. We will also discuss the potentials and challenges to collaboratively extract, represent, integrate, and apply various sources of knowledge in radiation therapy planning in the foreseeable future.
This program is designed to meet the interest of radiation oncologists and radiation oncology residents.
- Describe the different models, tools, and technologies that are available and being developed to enable knowledge guidance in treatment planning.
- Describe the need for infrastructure development for collaborative knowledge building, modeling and sharing
- Describe the challenges, clinical benefits, and current limitations of knowledge-based auto planning and exciting opportunities for future development.
- Yaorong Ge, PhD is employed as a consultant at Wake Forest Baptist Health and has no financial relationships with a commercial interest.
- Kevin L. Moore, PhD is employed as a medical physicist at the University of California, San Diego and receives research grants and compensation from Varian Medical Systems.
- Jackie Wu, PhD is employed as a professor at Duke University Medical Center and receives research funding from NIH/NCI and Varian Medical Systems.
- Ying Xiao, PhD is employed as a professor at Jefferson Medical College and has no financial relationships with a commercial interest.
The person(s) above served as the developer(s) of this activity. Additionally, the Education and CME/MOC Committees had control over the content of this activity.
The American Society for Radiation Oncology (ASTRO) is accredited by the Accreditation Council of Continuing Medical Education to provide continuing education to physicians.
ASTRO is awarded Deemed Status by the American Board of Radiology to provide SA-CME as part of Part II Maintenance of Certification.
- 1.25 SA-CME
The American Society for Radiation Oncology (ASTRO) is accredited by the Accreditation Council of Continuing Medical Education to provide continuing medical education for physicians.
The American Society for Radiation Oncology (ASTRO) designates this Enduring material for a maximum of 1.25 AMA PRA Category 1 Credit™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
This activity meets the American Board of Radiology's criteria for a self-assessment activity in the ABR's Maintenance of Certification program. Participation in this course in combination with the successful completion of the corresponding assessment and course evaluation adheres to the guidelines established by the ABR for 1.25 self-assessment credits.
- 1.25 Certificate of AttendanceThis activity was designated for 1.25 AMA PRA Category 1 Credit™.
ASTRO members must log in to the ASTRO website to view and receive the discounted member rate.
- Nonmember: $105
- Member: $55
No refunds, extensions, or substitutions will be made for those participants who, for any reason, have not completed the course by the end of the qualification date. The qualification date for each course is listed in the course catalog on the ASTRO website under availability.
Participants using ASTRO's online courses to satisfy the requirement of a Maintenance of Certification (MOC) program should verify the number, type and availability dates of any course before making a purchase. No refunds, extensions, or substitutions will be made for participants who have purchased courses that do not align with their MOC requirement.
The course and its materials will only be available on the ASTRO website for that 3 year period regardless of purchase date. At the expiration of the qualification, participants will no longer have access to the course or its materials. ASTRO reserves the right to remove a course before the end of its qualification period.
One of the two latest versions of Google Chrome, Mozilla Firefox, Internet Explorer or Safari.